This paper introduces a new technique called species conservation for evolving paral-lel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current gen-eration are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimiza-tion problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.

The operator of a traffic control centre has to select the most appropriate traffic control action or combination of actions in a short time to manage the traffic network when non-recurrent road traffic congestion happens. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control actions that need to be considered during the decision making process. The identification of suitable control actions for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic actions for a number of control measures in a complicated situation is very time-consuming. This chapter presents an intelligent method for the real-time identification of road traffic actions which assists the human operator of the traffic control centre in managing the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural networks, and genetic algorithms. The system employs a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a genetic algorithm (GA) for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city in Saudi Arabia. The results obtained for the case study are promising and demonstrate that the proposed approach can provide an effective support for real-time traffic control.

New Artificial Intelligence (AI) approaches such as simulated annealing, genetic algorithms, simulated evolution, neural networks, tabu
search, fuzzy logic and their hybrid techniques have been applied in recent years to solving Generator Maintenance Scheduling (GMS)
problems. This paper presents a review of these AI approaches for the GMS problem. The formulation of problems and the
methodologies of solution are discussed and analysed. A case study is also included which presents the application of a genetic
algorithm to a test system based on a practical power system scenario.

The solution of generation scheduling (GS) problems
involves the determination of the unit commitment (UC) and
economic dispatch (ED) for each generator in a power system at
each time interval in the scheduling period. The solution
procedure requires the simultaneous consideration of these two
decisions. In recent years researchers have focused much
attention on new solution techniques to GS. This paper proposes
the application of a variety of genetic algorithm (GA) based
approaches and investigates how these techniques may be
improved in order to more quickly obtain the optimum or near
optimum solution for the GS problem. The results obtained show
that the GA-based hybrid approach offers an effective alternative
for solving realistic GS problems within a realistic timeframe.

Automatic test data generation helps testers to validate
software against user requirements more easily. Test
data can be generated from many sources; for example,
experience of testers, source program, or software
specification. Selecting a proper test data set is a
decision making task. Testers have to decide what test
data that they should use, and a heuristic technique is
needed to solve this problem automatically. In this
paper, we propose a framework for generating test data
from software specifications. The selected specification
is Unified Modeling Language (UML) state machine
diagram. UML state machine diagram describes a
system in term of state which can be changed when
there is an action occurring in the system. The
generated test data is a sequence of these actions.
These sequences of action help testers to know how they
should test the system. The quality of generated test
data is measured by the number of transitions which is
fired using the test data. The more transitions test data
can fire, the better quality of test data is. The number of
coverage transitions is also used as a feedback for a
heuristic search for a better test set. Genetic algorithms
(GAs) are selected for searching the best test data. Our
experimental results show that the proposed GA-based
approach can work well for generating test data for
some types of UML state machine diagrams.

This research presents an investigation into
the development of real time system identification using
intelligent algorithms. A simulation platform of a flexible
beam vibration using finite difference (FD) method is
used to demonstrate the real time capabilities of the
identification algorithms. A number of approaches and
algorithms for on line system identifications are explored
and evaluated to demonstrate the merits of the algorithms
for real time implementation. These approaches include
identification using (a) traditional recursive least square
(RLS) filter, (b) Genetic Algorithms (GAs) and (c)
adaptive Neuro_Fuzzy (ANFIS) model. The above
algorithms are used to estimate a linear discrete second
order model for the flexible beam vibration. The model is
implemented, tested and validated to evaluate and
demonstrate the merits of the algorithms for real time
system identification. Finally, a comparative performance
of error convergence and real time computational
complexity of the algorithms is presented and discussed
through a set of experiments.

In this paper we investigate and compare multi-objective and
weighted single objective approaches to a real world workforce scheduling
problem. For this difficult problem we consider the trade off in solution quality
versus population diversity, for different sets of fixed objective weights. Our
real-world workforce scheduling problem consists of assigning resources with
the appropriate skills to geographically dispersed task locations while satisfying
time window constraints. The problem is NP-Hard and contains the Resource
Constrained Project Scheduling Problem (RCPSP) as a sub problem. We investigate
a genetic algorithm and serial schedule generation scheme together with
various multi-objective approaches. We show that multi-objective genetic algorithms
can create solutions whose fitness is within 2% of genetic algorithms using
weighted sum objectives even though the multi-objective approaches know
nothing of the weights. The result is highly significant for complex real-world
problems where objective weights are seldom known in advance since it suggests
that a multi-objective approach can generate a solution close to the user
preferred one without having knowledge of user preferences.

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